{"id":"W2894893810","doi":"10.2139/ssrn.3202870","title":"Widgets and Wodgets: Technology Markets and R&amp;D Spillovers","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Innovation Policy and R&D","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Business; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001242616,0.0004403929,0.0006722247,0.001872272,0.001077524,0.007934217,0.0006044708,0.002355602,0.03849765],"category_scores_gemma":[0.008705835,0.0004015191,0.0005591161,0.002549771,0.002889019,0.01066558,0.003295691,0.001827992,0.001214894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007862719,"about_ca_system_score_gemma":0.0005287535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002105239,"about_ca_topic_score_gemma":0.002433592,"domain_scores_codex":[0.9995351,0.0001969325,0.00002079596,0.00009083379,0.00006630709,0.00009006583],"domain_scores_gemma":[0.9948506,0.003600611,0.0007587679,0.0002370553,0.0001754299,0.0003775711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002914066,0.0001466705,0.007790273,0.0001468715,0.00004805122,0.0002818753,0.0007064025,0.005242619,0.0005189522,0.9321826,0.003834168,0.04881009],"study_design_scores_gemma":[0.0000908783,0.00007859973,0.005802513,0.0001244856,0.00006613801,0.0001286692,0.001528107,0.01113453,0.000467639,0.9683546,0.01218748,0.00003639403],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6912849,0.01873586,0.06321662,0.03150619,0.0005224477,0.0001140773,0.0007629116,0.0002869855,0.1935701],"genre_scores_gemma":[0.9826282,0.002486272,0.002058695,0.0004158438,0.0001345986,0.00002394568,0.00006360838,0.0000173114,0.01217146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03849765,"threshold_uncertainty_score":0.1287875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01255742004665415,"score_gpt":0.2238852938562909,"score_spread":0.2113278738096367,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}